ASCL Code Record
[ascl:2108.008]
CatBoost: High performance gradient boosting on decision trees library
CatBoost is a machine learning method based on gradient boosting over decision trees and can be used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. It supports both numerical and categorical features and computation on CPU and GPU, and is fast and scalable. Visualization tools are also included in CatBoost.
- Code site:
- https://github.com/catboost/catboost
- Used in:
- https://ui.adsabs.harvard.edu/abs/2021arXiv210801074K
- Described in:
- https://ui.adsabs.harvard.edu/abs/2017arXiv170609516P
- Bibcode:
- 2021ascl.soft08008C
Preferred citation method:
Please see citation information here: https://github.com/catboost/catboost#reference-paper
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